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library( here )

## here() starts at /data/share/cdi/MLIV/Local repos/ppolskaia/MLIV

# Source function to generate file paths
source( here::here( "R/configs.R" ) )

# Load packages
source( here::here( "R/packages.R" ) )

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## Working directory is /data/share/cdi/MLIV/Local repos/ppolskaia/MLIV/tests

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## ##    Soren R. Kunzel, Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon. 2019.
## ##    ''Linear Aggregation in Tree-based Estimators.'' arXiv preprint 
## ##    arXiv:1906.06463. https://arxiv.org/abs/1906.06463 
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# Load helper functions           
source( here::here( "R/functions.R" ) )

load("/data/share/cdi/MLIV/Data/Simulated/Sim_Run_2024-01-08_ca/ca_50_1_CDML.RData")

df = ca_50_1_CDML

cdml = df %>%
  select(metric, id, CDML, queen)

# Turn off scientific notation
options( scipen = 999 )

# Filter the dataframe to include only rows where metric is "bias"
cdml_bias <- cdml %>%
  filter(metric == "bias")

cdml_bias_queen <- cdml_bias %>%
  filter(queen == "RF MOM DR")

# Create the ggplot object
p <- ggplot(cdml_bias_queen, aes(x = id, y = CDML)) +
  geom_bar(stat = "identity") +
  labs(x = "ID", y = "CDML Value", title = "Histogram of CDML for Bias Metric Faceted by Queen") +
  theme_minimal()

# Convert ggplot object to plotly object
ggplotly(p)